Part-Of-Speech (POS) Tagging API

What is Part-Of-Speech (POS) Tagging?

The goal of a Part-of-Speech tagger is to assign parts of speech to every token in your text. A token is a word, most of the time, but it can also be punctuation like "," "." ";"... In the end, the POS tagger will tell you whether a work is a noun, a verb, an adjective, etc. As language structures are radically different from one language to another, good POS taggers have to adapt to each language. Some languages are much harder to analyze than others.

Let's say you have the following sentence:

John Doe is a Go developer at Google.

The POS tagger will return the following:

Why Use Part-Of-Speech Tagging?

Data scientists working on natural language processing are often interested in performing Part-Of-Speech tagging in their research activities. They also often need to automatically parse dependencies (compounds, nominal subjects, determiners...).

Part-Of-Speech Tagging and Dependency Parsing with spaCy and Ginza

SpaCy is an excellent Natural Language Processing framework that performs fast and accurate Part-Of-Speech tagging and dependency parsing in many languages (see more here). The Ginza model based on spaCy, released by Megagon Labs, is performing extremely well in Japanese (see the project here).

Part-Of-Speech Tagging Inference API

Building an inference API for Part-Of-Speech tagging is an interesting step that can definitely make Natural Language Processing research easier. Thanks to an API, you can automate your Part-Of-Speech tagging and do it in any language, not necessarily in Python.

NLP Cloud's Part-Of-Speech tagging and Dependency Parsing API

NLP Cloud proposes a Part-Of-Speech tagging and dependency parsing API that gives you the opportunity to perform these operations out of the box, based on spaCy, and Ginza, with excellent performances. Part-Of-Speech tagging and dependency parsing are not very resource intensive, so the response time (latency), when performing them from the NLP Cloud API, is very good. You can do it in 15 different languages.

For more details, see our documentation about Part-Of-Speech tagging and dependency parsing here.